robust structure identification in semiparametric regression model

In this paper, we study a robust separation and estimation method, whichcan be used to simultaneously identify the linear and nonlinear parts of the partiallylinear additive model, and estimate them, in the presence of outliers. this model isfurther sparse in the sense that it is suitable for detection of non-effective covariateswith exactly zero estimates of their coefficients. the proposed method is applied toanalysis of the well-known boston housing prices data set which is a benchmark dataset in the literature of robust regression.